# dataset settings
dataset_type = "VOCDataset"
data_root = "data/VOCdevkit/"
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="Resize", img_scale=(384, 384), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="MultiScaleFlipAug",
         img_scale=(384, 384),
         flip=False,
         transforms=[
             dict(type="Resize", keep_ratio=True),
             dict(type="RandomFlip"),
             dict(type="Normalize", **img_norm_cfg),
             dict(type="Pad", size_divisor=32),
             dict(type="ImageToTensor", keys=["img"]),
             dict(type="Collect", keys=["img"]),
         ])
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=2,
    train=dict(type=dataset_type,
               ann_file=[
                   data_root + "VOC2007/ImageSets/Main/trainval.txt",
                   data_root + "VOC2012/ImageSets/Main/trainval.txt"
               ],
               img_prefix=[data_root + "VOC2007/", data_root + "VOC2012/"],
               pipeline=train_pipeline),
    val=dict(type=dataset_type,
             ann_file=data_root + "VOC2007/ImageSets/Main/test.txt",
             img_prefix=data_root + "VOC2007/",
             pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        # ann_file=[
        #     data_root + "VOC2007/ImageSets/Main/trainval.txt",
        #     data_root + "VOC2012/ImageSets/Main/trainval.txt"
        # ],
        # img_prefix=[data_root + "VOC2007/", data_root + "VOC2012/"],
        ann_file=data_root + "VOC2007/ImageSets/Main/test.txt",
        img_prefix=data_root + "VOC2007/",
        pipeline=test_pipeline))

evaluation = dict(interval=1, metric="mAP")
